SIVOCS | Preliminary Results of the Survey Analysis

class: center, middle, inverse, title-slide

# SIVOCS | Preliminary Results of the Survey Analysis
###

---

<style>

.center2 {
  margin: 0;
  position: absolute;
  top: 50%;
  left: 50%;
  -ms-transform: translate(-50%, -50%);
  transform: translate(-50%, -50%);
}

.large { font-size: 130% }
.small { font-size: 70% }

.remark-slide-content.hljs-default {
  border-top: 60px solid #23373B;
}

.remark-slide-content > h1 {
  font-size: 30px;
  margin-top: -75px;
}

</style>

# Sample, Respondents & Bias

.pull-left[
<img src="data:image/png;base64,#./table_2.png" width="100%" />

* Slight bias towards female participants
* Slight bias towards SSH and Natural Sciences
]

.pull-right[
<img src="data:image/png;base64,#../sample_respondent.svg" width="200%" />
]

---

# Familiarity with SI and Transdisciplinarity

.right[.small[N={352, 112, 360}]]

* *Experience with transdisciplinary research* and *projct's contribution to SI* have relatively similar distributions, over 45 % of respondents rated equal or higher than 7 for each.  
* *Familiarity with SI* generally assessed lower, i.e. over 60 % of respondents rated between 0 and 3.

---

# *Familiarity with SI* across scientific domains

* *Familiarity with SI* differs across scientific domains (ANOVA test: p < 0.05)
* *Biology and Medicine* and *Math., Natur, and Eng. Sci.* are similar (pairwise t-test: p > 0.05)
* *Humanities and Social Sciences* are significantly different than the others (pairwise t-test with each: p < 0.05)

---

# Intention & Agency: Motivation

.right[.small[N = {360, 354, 365}]]

* Most projects (84%) driven by academic motivation (better understanding a phenomenon),
* followed by 62 % strongly motivated to directly address a problem;
* *Improving the human condition/welfare* more balanced across answer options

---
# Intention & Agency – Impulses from the Non-academic World
.pull-left[
###### *Addressing a specific problem* ... 
<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-13-1.svg" width="504" />

]

.pull-right[
###### Direct benef for the general population or a specific non-academic group
<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-14-1.svg" width="504" />

]

* *Medical/health* problems followed by *societal* ones are most often mentioned *impulses from the non-acadmic world*
* *Economic* problems least frequently mentioned category (only 29 participants)

---
# Transdisciplinary Aspects: Inter-/Transdisciplinary Involvement

.right[.small[N = 360]]

* Interdisciplinary involvement common: researchers from other disciplines quite centrally involved (41 %)
* Transdisciplinary involvement
  + not as central but noteworthy for fundamental research projects
  + similar across different groups in terms of extent and depth of involvement (*marginal* / *central*)

---

# Transdisciplinary Aspects: Nature of Involvement

.right[.small[N={74, 80, 82, 51, 94, 114}]]

* Transdisciplinary involvement mostly *consultative* or *contributory* in nature.
* *Collaborative* processes more likely in projects where *welfare-/education providing institutions* or *company/ business representatives* are relevant

---

# Transdisciplinary Aspects: Target Group Goals

##### *Your project has* ...

* *Empowering people* most frequently selected (170) category among goals for target group
* *Enabling diversity and exchange of different perspectives* second most freqeuntly selected (151)
* *Worked towards improving people lives* least selected category (167)

---

# Regulatory Framework: Open Science Concepts

###### Norms, requirements, practices applicable to research projects:

<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-20-1.svg" width="1152" />
* *Open access to publications* most frequently selected category (326 times),
* followed by *open access to data* (234 times)

---

# Regulatory Framework: Gender Dimension & Support for Policy-Making

.pull-left[

###### Explicit consideration of gender in research

<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-21-1.svg" width="504" />
* Vast majority of participants (275) stated *gender* was no explicit consideration in their project.

]

.pull-right[

###### Aim: support of evidence-based policy-making
<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-22-1.svg" width="504" />
* ~ a third of projects aimed at supporting evidence-based policy-making

]

---
# Outcome-orientation: Direct Contribution
.right[.small[N = 360]]

* A tiny minority directly contributed to *new or better services, products, or processes* for civil society organisations
* However, 18 % of respondents stated to have strongly contributed to benefit the general population

---

# Outcome-orientation: Intended Effects

###### Intended short- or long-term change benefiting specific target groups or the general population:

* Improving the *understanding* in the general population as well as raising the *awareness* by far the most frequently selected category (79 and 50 times)
* *Changing behaviour* most frequently stated for policy-makers/public administrators

---

# Outcome-orientation: Impact Statements

.right[.small[N = {172, 149, 140}]]

* Academic dimension most frequently chosen,
* followed by *issues not widely known in society* and *generate a deeper understanding of a specific social issues*.

---

# Correlation Matrix: Impact Assessment vs. Motivation

.small[**mot.**: Motivation to *understand a phenomenon* or *directly address a problem* (natural, technical, economic, or social)

**impactGr.**: direct contribution to *new or better services, products, processes, or ways of doing things* that were targeted towards ...]

.pull-left[

]

.pull-right[

* *Motivations* do not correlate highly with *direct contribution to new or better services, products, processes, or ways of doing things* by projects
* *Motivation to improve human condition* shows significant correlations with almost all impact statements (excl. purely academic contribution)
* Strongest positive correlation is between *the benefit to general population* and *motivation to improve human condition* (0.5)

]

---

# Further steps

##### Ongoing analytical tasks

* Statistical analyses of relationships between variables / correlations
* Linear and logistic regression models
* Dimension reduction, factor analysis, principal component analysis

##### Outlook

* Build statistical models to predict "degree" of SI
* Compile analytical results of survey into a report (part of the final report)
* Ideally: test with more quantitative data
* Liaise with SNSF to shape messages to target audiences in 2022
* Publish results in scientific journal (with approval of SNSF)

---
class:clear

.center[
### Many thanks for your attention!
]

Contact information:
* Utku Demir <demir@zsi.at>
* Dietmar Lampert <lampert@zsi.at> (presenter)
* Klaus Schuch <schuch@zsi.at>

ZSI - Centre for Social Innovation - https://zsi.at/en

---

# Familiarity with SI and Transdisciplinarity vs Impact Assessment, Correlation Matrix

.small[**involv.**: In your research processes, did you actively involve one or more of the following groups(*)?

**policy.uptake**: From your perspective, to what extent were project results taken up by policy-making and/or public administration and/or governmental agencies?

**scalability**: How would you assess in the long term the scalability of the results generated by your project?
]

.pull-left[
<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-32-1.svg" width="504" />
]

.pull-right[

* There is almost no stat. significant correlations between up-, out-scalability and inter-/transdisciplinary involvement.
* Deep-scalability correlates with several of the trans-/disciplinary involvement with the highest being the involvement of citizens (0.4).
* Policy uptake also correlates relatively strongly with transdisciplinary involvement in the project, especially when policymakers and civil society organisations involved.

]

---

# Correlation Matrix: Motivation Types vs Trans-, Interdisciplinary Involvement

.small[
**involv.**: In your research processes, did you actively involve one or more of the following groups(*)?

**mot.**: When you designed your project, to what degree were you motivated to…
]

.pull-left[
<img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-33-1.svg" width="504" />
]

.pull-right[

* Motivation to *understand a natural, technical, economic, or social phenomenon better* and *directly address a natural, technical, economic, or social problem* do not necessarily correlate with inter-/transdisciplinary involvements.

* Although weakly, motivation to improve human condition/welfare correlates with all of the inter-/transdisciplinary involvements with the strongest ones being *civil society* and *civil society* organisations.

]

---

# Transdisciplinary Experience vs. Contribution to SI by Sciencetific Domains

---

# Scaleability vs. Transdisciplinarity by Scientific Domains

---